{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebd66700",
   "metadata": {},
   "source": [
    "## Demo_Lips\n",
    "This is a demo for visualizing the lipschitz constant of a Neuron Network\n",
    "\n",
    "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b950f4fc",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "! python ../../attack/badnet.py --save_folder_name badnet_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f81f973",
   "metadata": {},
   "source": [
    "or run the following command in your terminal\n",
    "\n",
    "```python attack/badnet.py --save_folder_name badnet_demo```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bd9f5a",
   "metadata": {},
   "source": [
    "### Step 1: Import modules and set arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71b7087b",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import yaml\n",
    "import torch\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib\n",
    "from matplotlib.patches import Rectangle, Patch\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "sys.path.append(\"../../\")\n",
    "sys.path.append(os.getcwd())\n",
    "from visual_utils import *\n",
    "from utils.aggregate_block.dataset_and_transform_generate import (\n",
    "    get_transform,\n",
    "    get_dataset_denormalization,\n",
    ")\n",
    "from utils.aggregate_block.fix_random import fix_random\n",
    "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n",
    "from utils.save_load_attack import load_attack_result\n",
    "from utils.defense_utils.dbd.model.utils import (\n",
    "    get_network_dbd,\n",
    "    load_state,\n",
    "    get_criterion,\n",
    "    get_optimizer,\n",
    "    get_scheduler,\n",
    ")\n",
    "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2fb719c7",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "### Basic setting: args\n",
    "args = get_args(True)\n",
    "\n",
    "########## For Demo Only ##########\n",
    "args.yaml_path = \"../../\"+args.yaml_path\n",
    "args.result_file_attack = \"badnet_demo\"\n",
    "######## End For Demo Only ##########\n",
    "\n",
    "with open(args.yaml_path, \"r\") as stream:\n",
    "    config = yaml.safe_load(stream)\n",
    "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n",
    "args.__dict__ = config\n",
    "args = preprocess_args(args)\n",
    "fix_random(int(args.random_seed))\n",
    "\n",
    "save_path_attack = \"../..//record/\" + args.result_file_attack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f959b510",
   "metadata": {},
   "source": [
    "### Step 2: Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8b67ac9",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "loading...\n",
      "Load model preactresnet18 from badnet_demo\n"
     ]
    }
   ],
   "source": [
    "# Load result\n",
    "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n",
    "\n",
    "# Load model\n",
    "model_visual = generate_cls_model(args.model, args.num_classes)\n",
    "model_visual.load_state_dict(result_attack[\"model\"])\n",
    "model_visual.to(args.device)\n",
    "# !!! Important to set eval mode !!!\n",
    "model_visual.eval()\n",
    "print(f\"Load model {args.model} from {args.result_file_attack}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc952077",
   "metadata": {},
   "source": [
    "### Step 3: Plot lipschitz constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "94612903",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting lipschitz constant\n"
     ]
    }
   ],
   "source": [
    "############## lipschitz constant ##################\n",
    "print(\"Plotting lipschitz constant\")\n",
    "\n",
    "module_dict = dict(model_visual.named_modules())\n",
    "\n",
    "\n",
    "module_names = module_dict.keys()\n",
    "\n",
    "# Plot Conv2d or Linear\n",
    "module_visual = [i for i in module_dict.keys() if isinstance(\n",
    "    module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97de2b56",
   "metadata": {},
   "source": [
    "### Step 4: Collect Lips Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fec3fb2",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Lips Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting Lips BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting Lips Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting Lips Linear(in_features=512, out_features=10, bias=True)\n"
     ]
    }
   ],
   "source": [
    "df = None\n",
    "\n",
    "max_num_neuron = 0\n",
    "for module_name in module_visual:\n",
    "    target_layer = module_dict[module_name]\n",
    "    \n",
    "    print(f'Collecting Lips {target_layer}')\n",
    "    if isinstance(target_layer, torch.nn.Linear):\n",
    "        channel_lips = []\n",
    "        for idx in range(target_layer.weight.shape[0]):\n",
    "            w = target_layer.weight[idx].reshape(target_layer.weight.shape[1], -1)\n",
    "            # Just norm of weight for linear layer\n",
    "            channel_lips.append(torch.svd(w)[1].max())\n",
    "        channel_lips = torch.Tensor(channel_lips)\n",
    "\n",
    "    elif isinstance(target_layer, torch.nn.BatchNorm2d):\n",
    "        std = target_layer.running_var.sqrt()\n",
    "        weight = target_layer.weight\n",
    "\n",
    "        channel_lips = []\n",
    "        for idx in range(weight.shape[0]):\n",
    "            w = conv.weight[idx].reshape(conv.weight.shape[1], -1) * (weight[idx]/std[idx]).abs()\n",
    "            channel_lips.append(torch.svd(w)[1].max())\n",
    "        channel_lips = torch.Tensor(channel_lips)\n",
    "\n",
    "        \n",
    "        # Convolutional layer should be followed by a BN layer by default\n",
    "    elif isinstance(target_layer, torch.nn.Conv2d):\n",
    "        conv = target_layer    \n",
    "            \n",
    "        channel_lips = []\n",
    "        for idx in range(target_layer.weight.shape[0]):\n",
    "            w = target_layer.weight[idx].reshape(target_layer.weight.shape[1], -1)\n",
    "            channel_lips.append(torch.svd(w)[1].max())\n",
    "        channel_lips = torch.Tensor(channel_lips)\n",
    "    else:\n",
    "        assert False, \"Unknown layer type\"\n",
    "\n",
    "    for neuron_i in range(channel_lips.shape[0]):\n",
    "        base_row = {}\n",
    "        base_row['layer'] = module_name\n",
    "        base_row['Neuron'] = neuron_i\n",
    "        base_row['Lips'] = channel_lips[neuron_i].item()\n",
    "        if df is None:\n",
    "            df = pd.DataFrame.from_dict([base_row])\n",
    "        else:\n",
    "            df.loc[df.shape[0]] = base_row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3760b9",
   "metadata": {},
   "source": [
    "### Step 5: Show the Lips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81bbb857",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ploting conv1\n",
      "ploting layer1.0.bn1\n",
      "ploting layer1.0.conv1\n",
      "ploting layer1.0.bn2\n",
      "ploting layer1.0.conv2\n",
      "ploting layer1.1.bn1\n",
      "ploting layer1.1.conv1\n",
      "ploting layer1.1.bn2\n",
      "ploting layer1.1.conv2\n",
      "ploting layer2.0.bn1\n",
      "ploting layer2.0.conv1\n",
      "ploting layer2.0.bn2\n",
      "ploting layer2.0.conv2\n",
      "ploting layer2.0.shortcut.0\n",
      "ploting layer2.1.bn1\n",
      "ploting layer2.1.conv1\n",
      "ploting layer2.1.bn2\n",
      "ploting layer2.1.conv2\n",
      "ploting layer3.0.bn1\n",
      "ploting layer3.0.conv1\n",
      "ploting layer3.0.bn2\n",
      "ploting layer3.0.conv2\n",
      "ploting layer3.0.shortcut.0\n",
      "ploting layer3.1.bn1\n",
      "ploting layer3.1.conv1\n",
      "ploting layer3.1.bn2\n",
      "ploting layer3.1.conv2\n",
      "ploting layer4.0.bn1\n",
      "ploting layer4.0.conv1\n",
      "ploting layer4.0.bn2\n",
      "ploting layer4.0.conv2\n",
      "ploting layer4.0.shortcut.0\n",
      "ploting layer4.1.bn1\n",
      "ploting layer4.1.conv1\n",
      "ploting layer4.1.bn2\n",
      "ploting layer4.1.conv2\n",
      "ploting linear\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f91c98e2880>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2664x3672 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_x0 = 0\n",
    "height = 1\n",
    "width = 1\n",
    "vmin = 0\n",
    "if args.normalize_by_layer:\n",
    "    vmax = 1\n",
    "else:\n",
    "    vmax = df.Lips.max()\n",
    "max_num_neuron = df.Neuron.max()\n",
    "\n",
    "\n",
    "norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)\n",
    "mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.Oranges)\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    figsize=(int(len(module_visual)), int(max_num_neuron/10)))\n",
    "\n",
    "ax.plot([0, 0], [0, 0])\n",
    "\n",
    "for module_name in module_visual:\n",
    "    print(f'ploting {module_name}')\n",
    "    y_0 = 0\n",
    "    layer_info = df[df.layer == module_name]\n",
    "    layer_lips_max = layer_info['Lips'].max()\n",
    "    total_neuron = layer_info.shape[0]\n",
    "    for neuron_i in range(total_neuron):\n",
    "        x_0 = start_x0\n",
    "        base_row = layer_info.iloc[neuron_i]\n",
    "        if args.normalize_by_layer:\n",
    "            ax.add_patch(Rectangle((x_0, y_0), width, height,\n",
    "                facecolor=mapper.to_rgba(base_row['Lips']/layer_lips_max),\n",
    "                fill=True,\n",
    "                lw=5,\n",
    "                alpha=0.8))\n",
    "\n",
    "        else:\n",
    "            ax.add_patch(Rectangle((x_0, y_0), width, height,\n",
    "                            facecolor=mapper.to_rgba(base_row['Lips']),\n",
    "                            fill=True,\n",
    "                            lw=5,\n",
    "                            alpha=0.8))\n",
    "\n",
    "        y_0 += 1.5*height\n",
    "    start_x0 += 1.5*width\n",
    "x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))]\n",
    "y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)]\n",
    "\n",
    "ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1))\n",
    "ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1))\n",
    "ax.set_xticks(x_loc, module_visual, rotation=270)\n",
    "ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10])\n",
    "ax.set_title(f'Lips of Attack Model')\n",
    "ax.set_ylabel('Neuron')\n",
    "ax.set_xlabel('Layer')\n",
    "\n",
    "cb_ax = fig.add_axes([0.15, 0.9, 0.7, 0.01])\n",
    "\n",
    "fig.colorbar(mapper,\n",
    "             cax=cb_ax, orientation=\"horizontal\", label='Lips')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
